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Bootstrapping
The first column, labeled S.E., contains bootstrap estimates of standard errors.
These estimates may be compared to the approximate standard error estimates
obtained by maximum likelihood.
The second column, labeled S.E.-S.E., gives an approximate standard error for the
bootstrap standard error estimate itself.
The column labeled Mean represents the average parameter estimate computed
across bootstrap samples. This bootstrap mean is not necessarily identical to the
original estimate.
The column labeled Bias gives the difference between the original estimate and the
mean of estimates across bootstrap samples. If the mean estimate across bootstrapped
samples is higher than the original estimate, then Bias will be positive.
The last column, labeled S.E.-Bias, gives an approximate standard error for the bias
estimate.
Modeling in VB.NET
The following program (Ex19.vb) fits the model of Example 19 and performs a
bootstrap with 500 bootstrap samples. The program is the same as in Example 8, but
with an additional
Bootstrap line.
The line
Sem.Bootstrap(500) requests bootstrap standard errors based on 500 bootstrap
samples.
Sub Main()
Dim Sem As New AmosEngine
Try
Sem.TextOutput()
Sem.Bootstrap(500)
Sem.Standardized()
Sem.Smc()
Sem.BeginGroup(Sem.AmosDir & "Examples\Grnt_fem.sav")
Sem.AStructure("visperc = (1) spatial + (1) err_v")
Sem.AStructure("cubes = spatial + (1) err_c")
Sem.AStructure("lozenges = spatial + (1) err_l")
Sem.AStructure("paragrap = (1) verbal + (1) err_p")
Sem.AStructure("sentence = verbal + (1) err_s")
Sem.AStructure("wordmean = verbal + (1) err_w")
Sem.FitModel()
Finally
Sem.Dispose()
End Try
End Sub